The evolution of high-performance computing is associated with the growth of energy consumption. Performance of cluster computes (is increased via rising in performance and the number of used processors, GPUs, and coprocessors. An increment in the number of computing elements results in significant growth of energy consumption. Power grids limits for supercomputer centers (SCC) are driving the transition to more energy-efficient solutions. Often upgrade of computing resources is done step-by-step, i.e. parts of older supercomputers are removed from service and replaced with newer ones. A single SCC at any time can operate several computing systems with different performance and power consumption. That is why the problem of scheduling parallel programs execution on SCC resources to optimize energy consumption and minimize the increase in execution time (energy-efficient scheduling) is important. The goal of the presented work was the development of a new energy-efficient algorithm for scheduling computing resources at SCC. To reach the goal the authors analyzed methods of scheduling computing resources in a shared facility, including energy consumption minimizing methods. The study made it possible to formulate the problem of energy-efficient scheduling for a set of CCs and propose an algorithm for its solution. Experiments on NPB benchmarks allowed achieving significant reduction in energy consumption with a minor increase of runtime.